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肌肉疲劳对机器人辅助上肢训练期间肌电图与运动学相关性的影响。

Influence of muscle fatigue on electromyogram-kinematic correlation during robot-assisted upper limb training.

作者信息

Poyil Azeemsha T, Steuber Volker, Amirabdollahian Farshid

机构信息

School of Engineering and Computer Science, University of Hertfordshire, Hatfield, UK.

出版信息

J Rehabil Assist Technol Eng. 2020 Mar 16;7:2055668320903014. doi: 10.1177/2055668320903014. eCollection 2020 Jan-Dec.

Abstract

INTRODUCTION

Studies on adaptive robot-assisted upper limb training interactions do not often consider the implications of muscle fatigue sufficiently.

METHODS

To explore this, we initially assessed muscle fatigue in 10 healthy subjects using two electromyogram features, namely average power and median power frequency, during an assist-as-needed interaction with HapticMaster robot. Since robotic assistance resulted in a variable fatigue profile across participants, a completely tiring experiment, without a robot in the loop, was also designed to confirm the results.

RESULTS

A significant increase in average power and a decrease in median frequency were observed in the most active muscles. Average power in the frequency band of 0.8-2.5 Hz and median frequency in the band of 20-450 Hz are potential fatigue indicators. Also, comparing the Spearman's correlation coefficients (between the electromyogram average power and the kinematic force) across trials indicated that correlation was reduced as individual muscles were fatigued.

CONCLUSIONS

Confirming fatigue indicators, this study concludes that robotic assistance based on user's performance resulted in lesser muscle fatigue, which caused an increase in electromyogram-force correlation. We now intend to utilise the electromyogram and kinematic features for auto-adaptation of therapeutic human-robot interactions.

摘要

引言

关于自适应机器人辅助上肢训练交互的研究通常没有充分考虑肌肉疲劳的影响。

方法

为了探究这一点,我们首先在与触觉大师机器人进行按需辅助交互期间,使用平均功率和中位功率频率这两个肌电图特征,对10名健康受试者的肌肉疲劳进行了评估。由于机器人辅助导致不同参与者的疲劳情况各异,因此还设计了一个完全疲劳实验,即不使用机器人参与,以确认实验结果。

结果

在最活跃的肌肉中观察到平均功率显著增加,中位频率降低。0.8 - 2.5赫兹频段的平均功率和20 - 450赫兹频段的中位频率是潜在的疲劳指标。此外,比较各试验中肌电图平均功率与运动力之间的斯皮尔曼相关系数表明,随着单个肌肉疲劳,相关性降低。

结论

本研究证实了疲劳指标,得出基于用户表现的机器人辅助导致肌肉疲劳程度较低,进而使肌电图与力的相关性增加的结论。我们现在打算利用肌电图和运动学特征实现治疗性人机交互的自动适应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e773/7079312/e591b18c4179/10.1177_2055668320903014-fig1.jpg

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